DOI QR코드

DOI QR Code

Development Model for Estimating Critical Path Probability of Element Path in PERT

PERT 요소공정의 주경로 확률 산정 모델 개발

  • 윤득노 (서울대학교 생태조경.지역시스템공학부 대학원) ;
  • 김태곤 (서울대학교 생태조경.지역시스템공학부 대학원) ;
  • 한이철 (서울대학교 생태조경.지역시스템공학부 대학원) ;
  • 이정재 (서울대학교 조경.지역시스템공학부, 서울대학교 농업생명과학연구원)
  • Received : 2010.01.15
  • Accepted : 2010.03.09
  • Published : 2010.03.31

Abstract

The PERT is one form of probabilistic network and can have many critical paths in the concept of each work has dispersed complete time. Here we propose two operators to estimate the probabilistic complete time about serial and parallel connections, and in each junction node, probability of critical path is estimated by new operator. Then we compare the estimated results with robability of critical path with deterministic CPM and Monte Carlo simulation (MCS). Our results show that all paths in PERT can be critical path, and proposed operators are efficient and accurate probabilistic calculators compare MCS result.

Keywords

References

  1. Burt, J. M. Jr., 1970. Monte Carlo techniques for stochastic network analysis, In Winter Simulation Conference, No. 9, 146-153. New York, United States.: IEEE.
  2. Burt, J. M. and M. B. Garman, JR., 1971. Conditional Monte Carlo: A simulation technique for stochastic network analysis, In Management Science 8(3): 207-217. https://doi.org/10.1287/mnsc.18.3.207
  3. Chen, S. and T. Chang, 2001. Finding multiple possible critical path using fuzzy PERT, In Institute of Electrical and Electronics Engineers 31(6): 930-937. https://doi.org/10.1109/3477.969496
  4. Chen, Y., Dan R. and Kwei T., 1997. Critical path in an activity network with time constraints, In European Journal of Operational Research 100(1): 122-133. https://doi.org/10.1016/S0377-2217(96)00140-3
  5. Dodin, B., 1984. Determining the K most critical paths in PERT networks, In Operator Research Society of America 32(4): 859-877. https://doi.org/10.1287/opre.32.4.859
  6. Dodin, B. and S. E. Elmaghraby 1985. Approximating the distribution functions in stochastic networks, In Computer and Operation Research 12(3): 251-264. https://doi.org/10.1016/0305-0548(85)90024-3
  7. Dodin, B. and M. Sirvanci., 1990. Stochastic networks and the extreme value distribution, In Computer and Operation Research 17(4): 397-409. https://doi.org/10.1016/0305-0548(90)90018-3
  8. Elmaghraby S. E., 2000. On criticality and sensitivity in activity networks, In European Journal of Operational Research 127(2): 220-238. https://doi.org/10.1016/S0377-2217(99)00483-X
  9. Fatemi Ghomi, S. M. T. and E. Teimouri, 2001. Path critical index and activity critical index in PERT networks, In European Journal of Operational Research141(1): 147-152. https://doi.org/10.1016/S0377-2217(01)00268-5
  10. Fulkerson, D. R., 1962. Expected critical path length in PERT networks, In Operations research 10(6): 808-817. https://doi.org/10.1287/opre.10.6.808
  11. Gong, D. and J. E. Rowings Jr, 1995. Calculation of safe float use in risk-analysis-oriented network scheduling, In International Journal of Project Management 13(3): 187-194. https://doi.org/10.1016/0263-7863(94)00004-V
  12. Lee, J., H. Yi, M. Park and J. Lee, 2005. An investigation of project completion time estimation method in PERT network for planning and management in large-scale systems, In Proc. of the Korean Society of Agricultural Engineerings Conference 695-699.
  13. Liang G. and T. Han, 2004. Fuzzy critical path for project network, In Information and Management Science 15(4): 29-40.
  14. Malcom, D. G., J. H. Roseboom, C. E. Clack and W. Fazar, 1959. Application of a technique for research and development program Evaluation, In OperationsResearch 7(5): 646-669. https://doi.org/10.1287/opre.7.5.646
  15. Ringer, L. J., 1969. Numerical operators for statistical PERT critical path analysis, In Management Science 16(2): 136-143 https://doi.org/10.1287/mnsc.16.2.B136
  16. Ringer, L. J., 1971. A statistical theory for PERT in which completion times of activities are inter-dependent, Management Science 17(11): 717-723. https://doi.org/10.1287/mnsc.17.11.717
  17. Robillard, P. and M. Trahan, 1977. The completion time of PERT networks, In Operations Research 25(1): 15-29. https://doi.org/10.1287/opre.25.1.15
  18. Sigal, C. E., A. A. B. Pritsker and J. J. Solberg, 1979. The use of cutsets in Monte Calros analysis of stochastic network, In Mathematic and Computers in Simulation 21(4): 376-384. https://doi.org/10.1016/0378-4754(79)90007-7
  19. Soroush H. M., 1994. The most critical path in a PERT network, In Operational Research Society 45(3): 287-300. https://doi.org/10.2307/2584163
  20. Van Slyke, R. M., 1963. Monte Carlo method and the PERT problem, In Operations Research and the Management Sciences 11(5): 839-860. https://doi.org/10.1287/opre.11.5.839
  21. Williams T. M., 1992. Criticality in stochastic networks, In Operational Research Society 43(4): 353-357. https://doi.org/10.1057/jors.1992.50
  22. Yang, H. and Y. Chen, 2000. Finding the critical path in an activity network with time-switch constraints, In European Journal of Operational Research 120: 603-613. https://doi.org/10.1016/S0377-2217(98)00390-7
  23. Yao, M. and W. Chu, 2007. A new approximation algorithm for obtaining the probability distribution function for project complement time, In Computers and Mathematics with Applications 54(2): 282-295. https://doi.org/10.1016/j.camwa.2007.01.036

Cited by

  1. Reliability Analysis of the Non-normal Probability Problem for Limited Area using Convolution Technique vol.55, pp.5, 2013, https://doi.org/10.5389/KSAE.2013.55.5.049